Modelling
# For indistinguishable Dyads
model_rows_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
'support_self_cw',
'support_partner_cw',
'isWeekendWeekend',
'got_JITAI_selfJITAIreceived',
'skilled_supportDaysafterIntervention',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb',
'studyGroupFirst3weeksinterventions',
'studyGrouplast3weeksinterventions'
)
model_rows_fixed_ordinal <- c(
model_rows_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed[2:length(model_rows_fixed)]
)
model_rows_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'nu',
'shape',
'sderr',
'sigma'
)
model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'Daily perceived persuasion target -> target',
'Daily perceived persuasion target -> agent',
'Daily perceived pressure target -> target',
'Daily perceived pressure target -> agent',
'Daily perceived pushing target -> target',
'Daily perceived pushing target -> agent',
'Day',
'Daily weartime',
'Daily perceived support target -> target',
'Daily perceived support target -> agent',
'Is a weekend',
'JITAI received',
'Days post skilled support intervention',
# '-- BETWEEN PERSON MAIN EFFECTS',
'Mean perceived persuasion target -> target',
'Mean Perceived persuasion target -> agent',
'Mean Perceived pressure target -> target',
'Mean Perceived pressure target -> agent',
'Mean Perceived pushing target -> target',
'Mean Perceived pushing target -> agent',
'Mean weartime',
'Difference study group 2',
'Difference study group 3'
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(Daily perceived persuasion target -> target)',
'sd(Daily perceived persuasion target -> agent)',
'sd(Daily perceived pressure target -> target)',
'sd(Daily perceived pressure target -> agent)',
'sd(Daily perceived pushing target -> target)',
'sd(Daily perceived pushing target -> agent)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'nu',
'shape',
'sderr',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
"Within-Person Effects" = c(2,14),
"Between-Person Effects" = c(15,23),
"Random Effects" = c(24, 30),
"Additional Parameters" = c(31,35)
)
rows_to_pack_ordinal <- list(
"Intercepts" = c(1,6),
"Within-Person Effects" = c(2+5,14+5),
"Between-Person Effects" = c(15+5,23+5),
"Random Effects" = c(24+5, 30+5),
"Additional Parameters" = c(31+5,35+6)
)
Subjective MVPA
range(df_double$pa_sub, na.rm = T)
## [1] 0 720
hist(df_double$pa_sub, breaks = 100)

Modelling using the gaussian family fails. Due to the many zeros,
transformations won’t help estimating the models. We employ the negative
binomial family.
formula <- bf(
pa_sub ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
#barriers_self_cw +
#support_self_cw + support_partner_cw +
#comf_self_cw + reas_self_cw +
isWeekend +
got_JITAI_self + skilled_support +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
studyGroup +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b"),
brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 20)", class = "shape"),
brms::set_prior("cauchy(0, 10)", class='sderr')
)
#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]
pa_sub <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::negbinomial(),
#control = list(adapt_delta = 0.99),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "NoExchangeProcesses_pa_sub")
)
pp_check(pa_sub, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -12067.8 177.4
## p_loo 41.0 3.3
## looic 24135.5 354.9
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.9]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3734 99.9% 433
## (0.7, 1] (bad) 2 0.1% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
plot(pa_sub, ask = FALSE)










summarize_brms(
pa_sub,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
IRR
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
|
Intercept
|
31.71*
|
20.56
|
49.41
|
1.003
|
2465.34
|
5312.65
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
0.98
|
0.88
|
1.10
|
1.000
|
13549.48
|
8955.59
|
|
Daily perceived persuasion target -> agent
|
0.93
|
0.84
|
1.04
|
1.000
|
16959.96
|
9134.23
|
|
Daily perceived pressure target -> target
|
1.02
|
0.78
|
1.37
|
1.000
|
14853.77
|
8626.76
|
|
Daily perceived pressure target -> agent
|
0.65*
|
0.52
|
0.85
|
1.000
|
14691.56
|
7780.60
|
|
Daily perceived pushing target -> target
|
1.03
|
0.89
|
1.22
|
1.000
|
13937.22
|
9633.32
|
|
Daily perceived pushing target -> agent
|
1.11
|
0.95
|
1.30
|
1.001
|
14611.46
|
9749.75
|
|
Day
|
0.98
|
0.64
|
1.54
|
1.000
|
9669.62
|
8932.25
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
1.04
|
0.85
|
1.28
|
1.000
|
16036.75
|
8748.99
|
|
JITAI received
|
0.96
|
0.74
|
1.26
|
1.000
|
17828.32
|
9129.79
|
|
Days post skilled support intervention
|
1.10
|
0.78
|
1.53
|
1.000
|
8504.93
|
8881.39
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
1.12
|
0.66
|
1.93
|
1.001
|
5570.12
|
7614.15
|
|
Mean Perceived persuasion target -> agent
|
1.22
|
0.70
|
2.13
|
1.001
|
5711.82
|
7802.64
|
|
Mean Perceived pressure target -> target
|
1.81
|
0.91
|
3.67
|
1.000
|
8662.89
|
8590.64
|
|
Mean Perceived pressure target -> agent
|
0.53
|
0.25
|
1.12
|
1.001
|
6973.14
|
8027.90
|
|
Mean Perceived pushing target -> target
|
0.48
|
0.18
|
1.21
|
1.001
|
6257.83
|
7999.08
|
|
Mean Perceived pushing target -> agent
|
0.60
|
0.27
|
1.33
|
1.000
|
8526.30
|
8621.42
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Difference study group 2
|
0.77
|
0.53
|
1.12
|
1.001
|
6005.38
|
8208.71
|
|
Difference study group 3
|
0.71
|
0.49
|
1.04
|
1.001
|
6188.10
|
7909.98
|
|
Random Effects
|
|
sd(Intercept)
|
0.68
|
0.50
|
0.91
|
1.00
|
3038.84
|
6473.22
|
|
sd(Daily perceived persuasion target -> target)
|
0.20
|
0.06
|
0.35
|
1.00
|
3222.10
|
2462.17
|
|
sd(Daily perceived persuasion target -> agent)
|
0.20
|
0.06
|
0.36
|
1.00
|
4636.32
|
4252.86
|
|
sd(Daily perceived pressure target -> target)
|
0.15
|
0.01
|
0.47
|
1.00
|
6811.14
|
4855.94
|
|
sd(Daily perceived pressure target -> agent)
|
0.15
|
0.01
|
0.45
|
1.00
|
7445.35
|
5691.62
|
|
sd(Daily perceived pushing target -> target)
|
0.22
|
0.01
|
0.51
|
1.00
|
3401.48
|
4148.64
|
|
sd(Daily perceived pushing target -> agent)
|
0.15
|
0.01
|
0.36
|
1.00
|
5060.24
|
5269.04
|
|
Additional Parameters
|
|
ar[1]
|
0.02
|
-0.94
|
0.94
|
1.00
|
13219.57
|
7395.63
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
0.14
|
0.13
|
0.14
|
1.00
|
16739.72
|
8265.30
|
|
sderr
|
0.05
|
0.00
|
0.13
|
1.00
|
7302.29
|
5677.79
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
Device Based MVPA
range(df_double$pa_obj, na.rm = T)
## [1] 5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)
hist(df_double$pa_obj_log, breaks = 50)

We tried negative binomial here as well for consistency, but the
model did not converge. Poisson also did not work. As we have no zeros
in this distribution, we log transform.
formula <- bf(
pa_obj_log ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
#barriers_self_cw +
#support_self_cw + support_partner_cw +
#comf_self_cw + reas_self_cw +
isWeekend +
got_JITAI_self + skilled_support +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
studyGroup +
day + weartime_self_cw + weartime_self_cb +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b"),
brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]
pa_obj_log <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.99),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "NoExchangeProcesses_pa_obj_log")
)
# plotting with the first imputed dataset.
pp_check(pa_obj_log, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 3337 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -2810.5 55.5
## p_loo 103.6 4.7
## looic 5620.9 111.1
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.4]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(pa_obj_log, ask = FALSE)











summarize_brms(
pa_obj_log,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
exp(Est.)
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
|
Intercept
|
111.72*
|
98.11
|
126.93
|
1.000
|
4652.13
|
7297.52
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
1.00
|
0.98
|
1.02
|
1.000
|
24791.36
|
9690.94
|
|
Daily perceived persuasion target -> agent
|
1.02*
|
1.00
|
1.05
|
1.000
|
26829.56
|
9092.49
|
|
Daily perceived pressure target -> target
|
0.99
|
0.94
|
1.04
|
1.001
|
28584.78
|
8359.60
|
|
Daily perceived pressure target -> agent
|
1.01
|
0.96
|
1.07
|
1.001
|
27637.42
|
9431.01
|
|
Daily perceived pushing target -> target
|
1.01
|
0.98
|
1.04
|
1.000
|
27858.19
|
9557.47
|
|
Daily perceived pushing target -> agent
|
0.98
|
0.95
|
1.01
|
1.001
|
25387.85
|
9302.99
|
|
Day
|
0.94
|
0.84
|
1.06
|
1.000
|
18061.18
|
9461.26
|
|
Daily weartime
|
1.00
|
1.00
|
1.00
|
1.001
|
12266.99
|
8465.38
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
1.01
|
0.97
|
1.06
|
1.001
|
30627.70
|
8692.92
|
|
JITAI received
|
0.95
|
0.90
|
1.01
|
1.001
|
28591.75
|
8944.94
|
|
Days post skilled support intervention
|
1.08
|
0.99
|
1.18
|
1.001
|
18912.21
|
8952.12
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
0.87
|
0.74
|
1.04
|
1.000
|
8969.15
|
9607.83
|
|
Mean Perceived persuasion target -> agent
|
1.07
|
0.90
|
1.28
|
1.000
|
8872.98
|
9593.22
|
|
Mean Perceived pressure target -> target
|
1.12
|
0.89
|
1.41
|
1.000
|
15223.03
|
9540.37
|
|
Mean Perceived pressure target -> agent
|
0.79*
|
0.64
|
0.97
|
1.001
|
13154.40
|
9642.93
|
|
Mean Perceived pushing target -> target
|
1.08
|
0.81
|
1.43
|
1.000
|
11425.72
|
9917.88
|
|
Mean Perceived pushing target -> agent
|
1.21
|
0.96
|
1.53
|
1.000
|
13354.93
|
10265.25
|
|
Mean weartime
|
1.00*
|
1.00
|
1.00
|
1.001
|
12881.03
|
10113.33
|
|
Difference study group 2
|
0.97
|
0.86
|
1.09
|
1.000
|
8990.05
|
9292.09
|
|
Difference study group 3
|
1.12
|
0.99
|
1.26
|
1.000
|
9200.34
|
9206.79
|
|
Random Effects
|
|
sd(Intercept)
|
0.27
|
0.20
|
0.35
|
1.00
|
3862.37
|
6162.94
|
|
sd(Daily perceived persuasion target -> target)
|
0.06
|
0.03
|
0.09
|
1.00
|
8282.89
|
7931.39
|
|
sd(Daily perceived persuasion target -> agent)
|
0.05
|
0.02
|
0.08
|
1.00
|
7019.88
|
5867.95
|
|
sd(Daily perceived pressure target -> target)
|
0.06
|
0.00
|
0.15
|
1.00
|
5222.75
|
7063.60
|
|
sd(Daily perceived pressure target -> agent)
|
0.04
|
0.00
|
0.10
|
1.00
|
8000.64
|
7186.93
|
|
sd(Daily perceived pushing target -> target)
|
0.07
|
0.01
|
0.15
|
1.00
|
3179.43
|
3294.71
|
|
sd(Daily perceived pushing target -> agent)
|
0.04
|
0.00
|
0.09
|
1.00
|
5489.70
|
6753.23
|
|
Additional Parameters
|
|
ar[1]
|
0.28
|
0.24
|
0.31
|
1.00
|
27463.61
|
8529.55
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.55
|
0.54
|
0.57
|
1.00
|
26453.44
|
8845.89
|
Affect
range(df_double$aff, na.rm = T)
## [1] 1 6
hist(df_double$aff, breaks = 15)

formula <- bf(
aff ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
#barriers_self_cw +
#support_self_cw + support_partner_cw +
#comf_self_cw + reas_self_cw +
isWeekend +
got_JITAI_self + skilled_support +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
studyGroup +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b"),
brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=6), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]
mood <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "NoExchangeProcesses_mood")
)
pp_check(mood, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -4820.2 63.7
## p_loo 95.7 4.4
## looic 9640.3 127.4
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.1]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.










summarize_brms(
mood,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
b
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
|
Intercept
|
5.00*
|
4.75
|
5.26
|
1.001
|
2327.62
|
4864.24
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
0.02
|
-0.01
|
0.05
|
1.000
|
20539.96
|
9342.17
|
|
Daily perceived persuasion target -> agent
|
-0.02
|
-0.05
|
0.01
|
1.001
|
20223.33
|
9689.95
|
|
Daily perceived pressure target -> target
|
-0.03
|
-0.11
|
0.04
|
1.001
|
21024.26
|
9182.26
|
|
Daily perceived pressure target -> agent
|
0.04
|
-0.04
|
0.11
|
1.000
|
21104.10
|
9259.70
|
|
Daily perceived pushing target -> target
|
-0.05*
|
-0.10
|
-0.01
|
1.000
|
20182.24
|
9308.33
|
|
Daily perceived pushing target -> agent
|
0.01
|
-0.04
|
0.06
|
1.001
|
20419.75
|
9782.10
|
|
Day
|
-0.05
|
-0.26
|
0.17
|
1.001
|
11973.48
|
9938.21
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
0.03
|
-0.04
|
0.09
|
1.000
|
21209.24
|
9311.37
|
|
JITAI received
|
0.01
|
-0.07
|
0.09
|
1.001
|
21438.55
|
9304.93
|
|
Days post skilled support intervention
|
-0.02
|
-0.17
|
0.14
|
1.001
|
11765.42
|
9514.17
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
0.21
|
-0.10
|
0.53
|
1.000
|
6222.63
|
8081.50
|
|
Mean Perceived persuasion target -> agent
|
0.06
|
-0.25
|
0.37
|
1.000
|
6163.32
|
8108.96
|
|
Mean Perceived pressure target -> target
|
0.04
|
-0.32
|
0.40
|
1.000
|
10686.98
|
8869.84
|
|
Mean Perceived pressure target -> agent
|
-0.09
|
-0.48
|
0.30
|
1.000
|
8518.94
|
8989.40
|
|
Mean Perceived pushing target -> target
|
-0.35
|
-0.87
|
0.18
|
1.000
|
7298.11
|
9283.56
|
|
Mean Perceived pushing target -> agent
|
-0.13
|
-0.59
|
0.32
|
1.000
|
8375.41
|
8988.22
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Difference study group 2
|
-0.26*
|
-0.48
|
-0.04
|
1.001
|
7351.79
|
8979.59
|
|
Difference study group 3
|
-0.02
|
-0.23
|
0.19
|
1.000
|
7757.12
|
8822.05
|
|
Random Effects
|
|
sd(Intercept)
|
0.62
|
0.49
|
0.80
|
1.00
|
3059.78
|
5908.11
|
|
sd(Daily perceived persuasion target -> target)
|
0.02
|
0.00
|
0.07
|
1.00
|
4888.52
|
5354.51
|
|
sd(Daily perceived persuasion target -> agent)
|
0.05
|
0.00
|
0.11
|
1.00
|
2662.16
|
3086.86
|
|
sd(Daily perceived pressure target -> target)
|
0.09
|
0.00
|
0.25
|
1.00
|
4398.46
|
5354.74
|
|
sd(Daily perceived pressure target -> agent)
|
0.15
|
0.01
|
0.33
|
1.00
|
3094.32
|
3669.04
|
|
sd(Daily perceived pushing target -> target)
|
0.09
|
0.02
|
0.16
|
1.00
|
4352.59
|
2736.33
|
|
sd(Daily perceived pushing target -> agent)
|
0.08
|
0.01
|
0.16
|
1.00
|
3779.84
|
3730.07
|
|
Additional Parameters
|
|
ar[1]
|
0.45
|
0.42
|
0.48
|
1.00
|
19434.96
|
8729.36
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.87
|
0.85
|
0.89
|
1.00
|
21076.97
|
9127.69
|
reactance
range(df_double$reactance, na.rm = T)
## [1] 0 5
hist(df_double$reactance, breaks = 6)

formula <- bf(
reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
#barriers_self_cw +
#support_self_cw + support_partner_cw +
#comf_self_cw + reas_self_cw +
isWeekend +
got_JITAI_self + skilled_support +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
studyGroup +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b"),
brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]
reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "NoExchangeProcesses_reactance")
)
pp_check(reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -1080.5 34.1
## p_loo 90.5 8.4
## looic 2160.9 68.3
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.9]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 743 98.3% 386
## (0.7, 1] (bad) 13 1.7% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
plot(reactance, ask = FALSE)










## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: reactance ~ persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw + isWeekend + got_JITAI_self + skilled_support + persuasion_self_cb + persuasion_partner_cb + pressure_self_cb + pressure_partner_cb + pushing_self_cb + pushing_partner_cb + studyGroup + day + (persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw | coupleID)
## autocor ~ ar(time = day, gr = coupleID:userID, p = 1)
## Data: data (Number of observations: 756)
## Draws: 4 chains, each with iter = 5000; warmup = 2000; thin = 1;
## total post-warmup draws = 12000
##
## Correlation Structures:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ar[1] 0.01 0.04 -0.08 0.09 1.00 16261 10139
##
## Multilevel Hyperparameters:
## ~coupleID (Number of levels: 38)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.57 0.09 0.43 0.76 1.00 6332 8116
## sd(persuasion_self_cw) 0.06 0.04 0.00 0.15 1.00 3432 6372
## sd(persuasion_partner_cw) 0.04 0.03 0.00 0.12 1.00 7787 6983
## sd(pressure_self_cw) 0.43 0.10 0.26 0.65 1.00 8383 9184
## sd(pressure_partner_cw) 0.25 0.16 0.02 0.63 1.00 3213 5167
## sd(pushing_self_cw) 0.08 0.06 0.00 0.22 1.00 3147 5424
## sd(pushing_partner_cw) 0.04 0.04 0.00 0.14 1.00 7904 7727
## cor(Intercept,persuasion_self_cw) -0.17 0.31 -0.70 0.50 1.00 12101 8942
## cor(Intercept,persuasion_partner_cw) 0.05 0.33 -0.59 0.67 1.00 22979 9001
## cor(persuasion_self_cw,persuasion_partner_cw) -0.01 0.35 -0.68 0.66 1.00 16677 9210
## cor(Intercept,pressure_self_cw) 0.29 0.20 -0.13 0.66 1.00 8113 9105
## cor(persuasion_self_cw,pressure_self_cw) -0.14 0.33 -0.72 0.56 1.00 3965 6019
## cor(persuasion_partner_cw,pressure_self_cw) -0.01 0.35 -0.67 0.66 1.00 3783 6791
## cor(Intercept,pressure_partner_cw) 0.20 0.25 -0.29 0.67 1.00 11119 9291
## cor(persuasion_self_cw,pressure_partner_cw) 0.03 0.35 -0.64 0.69 1.00 9266 8936
## cor(persuasion_partner_cw,pressure_partner_cw) -0.02 0.35 -0.68 0.66 1.00 8525 10027
## cor(pressure_self_cw,pressure_partner_cw) -0.01 0.31 -0.57 0.60 1.00 13805 9675
## cor(Intercept,pushing_self_cw) -0.03 0.27 -0.55 0.52 1.00 14473 8053
## cor(persuasion_self_cw,pushing_self_cw) -0.02 0.35 -0.66 0.65 1.00 10965 8235
## cor(persuasion_partner_cw,pushing_self_cw) 0.03 0.36 -0.64 0.69 1.00 9596 9096
## cor(pressure_self_cw,pushing_self_cw) 0.06 0.33 -0.58 0.66 1.00 13379 10395
## cor(pressure_partner_cw,pushing_self_cw) 0.02 0.36 -0.67 0.68 1.00 8888 10529
## cor(Intercept,pushing_partner_cw) 0.01 0.31 -0.61 0.62 1.00 21452 8335
## cor(persuasion_self_cw,pushing_partner_cw) 0.02 0.36 -0.67 0.69 1.00 19779 9389
## cor(persuasion_partner_cw,pushing_partner_cw) -0.03 0.36 -0.69 0.65 1.00 14266 9483
## cor(pressure_self_cw,pushing_partner_cw) -0.07 0.35 -0.71 0.61 1.00 17400 9525
## cor(pressure_partner_cw,pushing_partner_cw) 0.02 0.36 -0.66 0.68 1.00 13224 10505
## cor(pushing_self_cw,pushing_partner_cw) 0.01 0.36 -0.67 0.68 1.00 10715 10691
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.49 0.16 0.18 0.80 1.00 6934 9239
## persuasion_self_cw 0.03 0.03 -0.02 0.08 1.00 24993 9643
## persuasion_partner_cw -0.00 0.03 -0.06 0.06 1.00 21491 10145
## pressure_self_cw -0.02 0.05 -0.12 0.07 1.00 22846 8636
## pressure_partner_cw -0.01 0.07 -0.14 0.12 1.00 21887 9584
## pushing_self_cw 0.03 0.03 -0.03 0.09 1.00 23325 8602
## pushing_partner_cw -0.02 0.04 -0.10 0.06 1.00 22990 9574
## isWeekendWeekend -0.00 0.08 -0.16 0.15 1.00 29494 8887
## got_JITAI_selfJITAIreceived 0.05 0.11 -0.17 0.27 1.00 23888 8652
## skilled_supportDaysafterIntervention -0.05 0.13 -0.31 0.21 1.00 15180 10054
## persuasion_self_cb -0.10 0.20 -0.48 0.28 1.00 10258 9480
## persuasion_partner_cb 0.19 0.20 -0.21 0.59 1.00 11577 9929
## pressure_self_cb 0.21 0.23 -0.23 0.64 1.00 12411 9366
## pressure_partner_cb -0.13 0.24 -0.61 0.36 1.00 11927 9501
## pushing_self_cb -0.25 0.31 -0.86 0.37 1.00 9829 9017
## pushing_partner_cb -0.06 0.31 -0.68 0.55 1.00 11045 9483
## studyGroupFirst3weeksinterventions -0.01 0.13 -0.26 0.24 1.00 14544 10069
## studyGrouplast3weeksinterventions 0.19 0.15 -0.09 0.48 1.00 12160 9856
## day -0.11 0.20 -0.50 0.28 1.00 15201 10190
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.94 0.03 0.89 1.00 1.00 12997 8890
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
summarize_brms(
reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
b
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
|
Intercept
|
0.49*
|
0.18
|
0.80
|
1.000
|
6934.23
|
9239.36
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
0.03
|
-0.02
|
0.08
|
1.000
|
24992.65
|
9642.89
|
|
Daily perceived persuasion target -> agent
|
0.00
|
-0.06
|
0.06
|
1.000
|
21490.92
|
10144.92
|
|
Daily perceived pressure target -> target
|
-0.02
|
-0.12
|
0.07
|
1.000
|
22845.85
|
8636.16
|
|
Daily perceived pressure target -> agent
|
-0.01
|
-0.14
|
0.12
|
1.001
|
21886.84
|
9583.52
|
|
Daily perceived pushing target -> target
|
0.03
|
-0.03
|
0.09
|
1.001
|
23325.38
|
8601.96
|
|
Daily perceived pushing target -> agent
|
-0.02
|
-0.10
|
0.06
|
1.000
|
22989.85
|
9574.35
|
|
Day
|
-0.11
|
-0.50
|
0.28
|
1.000
|
15200.84
|
10190.37
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
0.00
|
-0.16
|
0.15
|
1.000
|
29494.30
|
8887.17
|
|
JITAI received
|
0.05
|
-0.17
|
0.27
|
1.000
|
23887.58
|
8652.34
|
|
Days post skilled support intervention
|
-0.05
|
-0.31
|
0.21
|
1.000
|
15180.24
|
10053.60
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
-0.10
|
-0.48
|
0.28
|
1.000
|
10257.56
|
9480.31
|
|
Mean Perceived persuasion target -> agent
|
0.19
|
-0.21
|
0.59
|
1.000
|
11576.88
|
9929.12
|
|
Mean Perceived pressure target -> target
|
0.21
|
-0.23
|
0.64
|
1.000
|
12410.93
|
9366.06
|
|
Mean Perceived pressure target -> agent
|
-0.13
|
-0.61
|
0.36
|
1.000
|
11926.72
|
9501.40
|
|
Mean Perceived pushing target -> target
|
-0.25
|
-0.86
|
0.37
|
1.000
|
9829.29
|
9016.87
|
|
Mean Perceived pushing target -> agent
|
-0.06
|
-0.68
|
0.55
|
1.000
|
11045.03
|
9482.70
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Difference study group 2
|
-0.01
|
-0.26
|
0.24
|
1.000
|
14544.38
|
10069.16
|
|
Difference study group 3
|
0.19
|
-0.09
|
0.48
|
1.000
|
12159.77
|
9855.57
|
|
Random Effects
|
|
sd(Intercept)
|
0.57
|
0.43
|
0.76
|
1.00
|
6332.22
|
8116.25
|
|
sd(Daily perceived persuasion target -> target)
|
0.06
|
0.00
|
0.15
|
1.00
|
3431.74
|
6372.27
|
|
sd(Daily perceived persuasion target -> agent)
|
0.04
|
0.00
|
0.12
|
1.00
|
7787.30
|
6983.34
|
|
sd(Daily perceived pressure target -> target)
|
0.43
|
0.26
|
0.65
|
1.00
|
8382.68
|
9183.84
|
|
sd(Daily perceived pressure target -> agent)
|
0.25
|
0.02
|
0.63
|
1.00
|
3213.34
|
5167.05
|
|
sd(Daily perceived pushing target -> target)
|
0.08
|
0.00
|
0.22
|
1.00
|
3146.70
|
5424.45
|
|
sd(Daily perceived pushing target -> agent)
|
0.04
|
0.00
|
0.14
|
1.00
|
7904.22
|
7726.65
|
|
Additional Parameters
|
|
ar[1]
|
0.01
|
-0.08
|
0.09
|
1.00
|
16260.83
|
10139.34
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sigma
|
0.94
|
0.89
|
1.00
|
1.00
|
12996.61
|
8890.45
|
Binary Version
introduce_binary_reactance <- function(data) {
data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
return(data)
}
df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
for (i in seq_along(implist)) {
implist[[i]] <- introduce_binary_reactance(implist[[i]])
}
}
formula <- bf(
is_reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
#barriers_self_cw +
#support_self_cw + support_partner_cw + support_partner_cw +
#comf_self_cw + reas_self_cw +
isWeekend +
got_JITAI_self + skilled_support +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
studyGroup +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b"),
brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1)
#brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]
is_reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = bernoulli(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "NoExchangeProcesses_is_reactance")
)
pp_check(is_reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -465.3 17.9
## p_loo 418.2 16.7
## looic 930.6 35.7
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.3]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3 0.4% 288
## (0.7, 1] (bad) 192 25.4% <NA>
## (1, Inf) (very bad) 561 74.2% <NA>
## See help('pareto-k-diagnostic') for details.
plot(is_reactance, ask = FALSE)










summarize_brms(
is_reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)
|
|
OR
|
l-95% CI
|
u-95% CI
|
Rhat
|
Bulk_ESS
|
Tail_ESS
|
|
Intercept
|
1.14
|
0.03
|
37.01
|
1.000
|
9740.12
|
9108.41
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
1.57
|
0.71
|
3.82
|
1.000
|
8664.83
|
8458.01
|
|
Daily perceived persuasion target -> agent
|
1.01
|
0.36
|
2.79
|
1.000
|
8440.17
|
8640.88
|
|
Daily perceived pressure target -> target
|
0.98
|
0.21
|
4.62
|
1.000
|
8347.75
|
8628.67
|
|
Daily perceived pressure target -> agent
|
0.78
|
0.10
|
5.77
|
1.000
|
8639.29
|
7851.79
|
|
Daily perceived pushing target -> target
|
1.45
|
0.56
|
4.16
|
1.000
|
8426.87
|
8506.75
|
|
Daily perceived pushing target -> agent
|
0.61
|
0.16
|
2.11
|
1.000
|
8099.87
|
8258.42
|
|
Day
|
1.84
|
0.01
|
338.50
|
1.000
|
9903.93
|
9141.30
|
|
Daily weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
0.54
|
0.04
|
6.05
|
1.001
|
9605.93
|
9331.71
|
|
JITAI received
|
10.28
|
0.42
|
301.32
|
1.000
|
8588.66
|
9096.21
|
|
Days post skilled support intervention
|
0.11
|
0.00
|
4.11
|
1.001
|
8738.36
|
8546.55
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
0.39
|
0.00
|
42.34
|
1.001
|
9946.86
|
8971.35
|
|
Mean Perceived persuasion target -> agent
|
11.82
|
0.13
|
1182.70
|
1.000
|
9919.40
|
9231.20
|
|
Mean Perceived pressure target -> target
|
3.49
|
0.02
|
488.58
|
1.001
|
10474.77
|
9629.59
|
|
Mean Perceived pressure target -> agent
|
0.61
|
0.00
|
133.79
|
1.000
|
9773.68
|
9121.66
|
|
Mean Perceived pushing target -> target
|
0.30
|
0.00
|
217.37
|
1.000
|
10497.97
|
9182.97
|
|
Mean Perceived pushing target -> agent
|
0.13
|
0.00
|
94.75
|
1.000
|
11053.69
|
9511.68
|
|
Mean weartime
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Difference study group 2
|
0.61
|
0.01
|
23.13
|
1.001
|
8866.64
|
8665.21
|
|
Difference study group 3
|
8.74
|
0.18
|
525.61
|
1.000
|
9131.08
|
9073.80
|
|
Random Effects
|
|
sd(Intercept)
|
7.26
|
5.04
|
9.78
|
1.00
|
8403.15
|
7339.19
|
|
sd(Daily perceived persuasion target -> target)
|
1.68
|
0.30
|
3.38
|
1.00
|
2993.92
|
2938.75
|
|
sd(Daily perceived persuasion target -> agent)
|
1.42
|
0.08
|
3.21
|
1.00
|
2742.87
|
4428.71
|
|
sd(Daily perceived pressure target -> target)
|
3.29
|
1.31
|
5.59
|
1.00
|
4930.17
|
4633.99
|
|
sd(Daily perceived pressure target -> agent)
|
1.28
|
0.05
|
3.70
|
1.00
|
7413.44
|
7611.55
|
|
sd(Daily perceived pushing target -> target)
|
0.91
|
0.04
|
2.42
|
1.00
|
3380.26
|
6170.55
|
|
sd(Daily perceived pushing target -> agent)
|
0.85
|
0.04
|
2.49
|
1.00
|
6790.81
|
8065.90
|
|
Additional Parameters
|
|
ar[1]
|
0.10
|
-0.09
|
0.29
|
1.00
|
3017.69
|
5131.00
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
9.56
|
6.04
|
14.10
|
1.00
|
4489.77
|
6495.99
|
|
sigma
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
Report All Models
if (report_ordinal) {
model_rows_random_final <- model_rows_random_ordinal
model_rows_fixed_final <- model_rows_fixed_ordinal
model_rownames_fixed_final <- model_rownames_fixed_ordinal
model_rownames_random_final <- model_rownames_random_ordinal
rows_to_pack_final <- rows_to_pack_ordinal
} else {
model_rows_random_final <- model_rows_random
model_rows_fixed_final <- model_rows_fixed
model_rownames_fixed_final <- model_rownames_fixed
model_rownames_random_final <- model_rownames_random
rows_to_pack_final <- rows_to_pack
}
all_models <- report_side_by_side(
pa_sub,
pa_obj_log,
mood,
reactance,
is_reactance,
model_rows_random = model_rows_random_final,
model_rows_fixed = model_rows_fixed_final,
model_rownames_random = model_rownames_random_final,
model_rownames_fixed = model_rownames_fixed_final
)
## [1] "pa_sub"
## [1] "pa_obj_log"
## [1] "mood"
## [1] "reactance"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
print_df(rows_to_pack = rows_to_pack_final) %>%
add_header_above(
c(" ", "Subjective MVPA" = 2,
"Device-Based MVPA" = 2,
"Mood" = 2,
"Reactance Gaussian" = 2,
"Reactance Dichotome" = 2)
)
export_xlsx(summary_all_models,
rows_to_pack = rows_to_pack_final,
file.path("Output", "SensitivityCovariates",
"NoExchangeProcesses_AllModels_SensCovariates.xlsx"),
merge_option = 'both',
simplify_2nd_row = TRUE,
colwidths = c(40, 7.4, 12.85, 7.4, 12.85,7.4, 12.85,7.4, 12.85,7.4, 12.85),
line_above_rows = c(1,2,3,28),
line_below_rows = c(-1))
summary_all_models
|
|
Subjective MVPA
|
Device-Based MVPA
|
Mood
|
Reactance Gaussian
|
Reactance Dichotome
|
|
|
IRR pa_sub
|
95% CI pa_sub
|
exp(Est.) pa_obj_log
|
95% CI pa_obj_log
|
b mood
|
95% CI mood
|
b reactance
|
95% CI reactance
|
OR is_reactance
|
95% CI is_reactance
|
|
Intercept
|
31.71*
|
[20.56, 49.41]
|
111.72*
|
[98.11, 126.93]
|
5.00*
|
[ 4.75, 5.26]
|
0.49*
|
[ 0.18, 0.80]
|
1.14
|
[0.03, 37.01]
|
|
Within-Person Effects
|
|
Daily perceived persuasion target -> target
|
0.98
|
[ 0.88, 1.10]
|
1.00
|
[ 0.98, 1.02]
|
0.02
|
[-0.01, 0.05]
|
0.03
|
[-0.02, 0.08]
|
1.57
|
[0.71, 3.82]
|
|
Daily perceived persuasion target -> agent
|
0.93
|
[ 0.84, 1.04]
|
1.02*
|
[ 1.00, 1.05]
|
-0.02
|
[-0.05, 0.01]
|
0.00
|
[-0.06, 0.06]
|
1.01
|
[0.36, 2.79]
|
|
Daily perceived pressure target -> target
|
1.02
|
[ 0.78, 1.37]
|
0.99
|
[ 0.94, 1.04]
|
-0.03
|
[-0.11, 0.04]
|
-0.02
|
[-0.12, 0.07]
|
0.98
|
[0.21, 4.62]
|
|
Daily perceived pressure target -> agent
|
0.65*
|
[ 0.52, 0.85]
|
1.01
|
[ 0.96, 1.07]
|
0.04
|
[-0.04, 0.11]
|
-0.01
|
[-0.14, 0.12]
|
0.78
|
[0.10, 5.77]
|
|
Daily perceived pushing target -> target
|
1.03
|
[ 0.89, 1.22]
|
1.01
|
[ 0.98, 1.04]
|
-0.05*
|
[-0.10, -0.01]
|
0.03
|
[-0.03, 0.09]
|
1.45
|
[0.56, 4.16]
|
|
Daily perceived pushing target -> agent
|
1.11
|
[ 0.95, 1.30]
|
0.98
|
[ 0.95, 1.01]
|
0.01
|
[-0.04, 0.06]
|
-0.02
|
[-0.10, 0.06]
|
0.61
|
[0.16, 2.11]
|
|
Day
|
0.98
|
[ 0.64, 1.54]
|
0.94
|
[ 0.84, 1.06]
|
-0.05
|
[-0.26, 0.17]
|
-0.11
|
[-0.50, 0.28]
|
1.84
|
[0.01, 338.50]
|
|
Daily weartime
|
NA
|
NA
|
1.00
|
[ 1.00, 1.00]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> target
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Daily perceived support target -> agent
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Is a weekend
|
1.04
|
[ 0.85, 1.28]
|
1.01
|
[ 0.97, 1.06]
|
0.03
|
[-0.04, 0.09]
|
0.00
|
[-0.16, 0.15]
|
0.54
|
[0.04, 6.05]
|
|
JITAI received
|
0.96
|
[ 0.74, 1.26]
|
0.95
|
[ 0.90, 1.01]
|
0.01
|
[-0.07, 0.09]
|
0.05
|
[-0.17, 0.27]
|
10.28
|
[0.42, 301.32]
|
|
Days post skilled support intervention
|
1.10
|
[ 0.78, 1.53]
|
1.08
|
[ 0.99, 1.18]
|
-0.02
|
[-0.17, 0.14]
|
-0.05
|
[-0.31, 0.21]
|
0.11
|
[0.00, 4.11]
|
|
Between-Person Effects
|
|
Mean perceived persuasion target -> target
|
1.12
|
[ 0.66, 1.93]
|
0.87
|
[ 0.74, 1.04]
|
0.21
|
[-0.10, 0.53]
|
-0.10
|
[-0.48, 0.28]
|
0.39
|
[0.00, 42.34]
|
|
Mean Perceived persuasion target -> agent
|
1.22
|
[ 0.70, 2.13]
|
1.07
|
[ 0.90, 1.28]
|
0.06
|
[-0.25, 0.37]
|
0.19
|
[-0.21, 0.59]
|
11.82
|
[0.13, 1182.70]
|
|
Mean Perceived pressure target -> target
|
1.81
|
[ 0.91, 3.67]
|
1.12
|
[ 0.89, 1.41]
|
0.04
|
[-0.32, 0.40]
|
0.21
|
[-0.23, 0.64]
|
3.49
|
[0.02, 488.58]
|
|
Mean Perceived pressure target -> agent
|
0.53
|
[ 0.25, 1.12]
|
0.79*
|
[ 0.64, 0.97]
|
-0.09
|
[-0.48, 0.30]
|
-0.13
|
[-0.61, 0.36]
|
0.61
|
[0.00, 133.79]
|
|
Mean Perceived pushing target -> target
|
0.48
|
[ 0.18, 1.21]
|
1.08
|
[ 0.81, 1.43]
|
-0.35
|
[-0.87, 0.18]
|
-0.25
|
[-0.86, 0.37]
|
0.30
|
[0.00, 217.37]
|
|
Mean Perceived pushing target -> agent
|
0.60
|
[ 0.27, 1.33]
|
1.21
|
[ 0.96, 1.53]
|
-0.13
|
[-0.59, 0.32]
|
-0.06
|
[-0.68, 0.55]
|
0.13
|
[0.00, 94.75]
|
|
Mean weartime
|
NA
|
NA
|
1.00*
|
[ 1.00, 1.00]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
Difference study group 2
|
0.77
|
[ 0.53, 1.12]
|
0.97
|
[ 0.86, 1.09]
|
-0.26*
|
[-0.48, -0.04]
|
-0.01
|
[-0.26, 0.24]
|
0.61
|
[0.01, 23.13]
|
|
Difference study group 3
|
0.71
|
[ 0.49, 1.04]
|
1.12
|
[ 0.99, 1.26]
|
-0.02
|
[-0.23, 0.19]
|
0.19
|
[-0.09, 0.48]
|
8.74
|
[0.18, 525.61]
|
|
Random Effects
|
|
sd(Intercept)
|
0.68
|
[ 0.50, 0.91]
|
0.27
|
[0.20, 0.35]
|
0.62
|
[0.49, 0.80]
|
0.57
|
[ 0.43, 0.76]
|
7.26
|
[ 5.04, 9.78]
|
|
sd(Daily perceived persuasion target -> target)
|
0.20
|
[ 0.06, 0.35]
|
0.06
|
[0.03, 0.09]
|
0.02
|
[0.00, 0.07]
|
0.06
|
[ 0.00, 0.15]
|
1.68
|
[ 0.30, 3.38]
|
|
sd(Daily perceived persuasion target -> agent)
|
0.20
|
[ 0.06, 0.36]
|
0.05
|
[0.02, 0.08]
|
0.05
|
[0.00, 0.11]
|
0.04
|
[ 0.00, 0.12]
|
1.42
|
[ 0.08, 3.21]
|
|
sd(Daily perceived pressure target -> target)
|
0.15
|
[ 0.01, 0.47]
|
0.06
|
[0.00, 0.15]
|
0.09
|
[0.00, 0.25]
|
0.43
|
[ 0.26, 0.65]
|
3.29
|
[ 1.31, 5.59]
|
|
sd(Daily perceived pressure target -> agent)
|
0.15
|
[ 0.01, 0.45]
|
0.04
|
[0.00, 0.10]
|
0.15
|
[0.01, 0.33]
|
0.25
|
[ 0.02, 0.63]
|
1.28
|
[ 0.05, 3.70]
|
|
sd(Daily perceived pushing target -> target)
|
0.22
|
[ 0.01, 0.51]
|
0.07
|
[0.01, 0.15]
|
0.09
|
[0.02, 0.16]
|
0.08
|
[ 0.00, 0.22]
|
0.91
|
[ 0.04, 2.42]
|
|
sd(Daily perceived pushing target -> agent)
|
0.15
|
[ 0.01, 0.36]
|
0.04
|
[0.00, 0.09]
|
0.08
|
[0.01, 0.16]
|
0.04
|
[ 0.00, 0.14]
|
0.85
|
[ 0.04, 2.49]
|
|
Additional Parameters
|
|
ar[1]
|
0.02
|
[-0.94, 0.94]
|
0.28
|
[0.24, 0.31]
|
0.45
|
[0.42, 0.48]
|
0.01
|
[-0.08, 0.09]
|
0.10
|
[-0.09, 0.29]
|
|
nu
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
shape
|
0.14
|
[ 0.13, 0.14]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
sderr
|
0.05
|
[ 0.00, 0.13]
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
9.56
|
[ 6.04, 14.10]
|
|
sigma
|
NA
|
NA
|
0.55
|
[0.54, 0.57]
|
0.87
|
[0.85, 0.89]
|
0.94
|
[ 0.89, 1.00]
|
NA
|
NA
|
Analyses were conducted using the R Statistical language (version
4.4.1; R Core Team, 2024) on Windows 11 x64 (build 22635)
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